No discovery phase that turns into a slide deck. We reproduce your baseline, run the agent, review every candidate, and ship PRs directly to your repo. Typical engagement: 4–8 weeks.
We pick one objective (pod cost, p99 latency, cold start, GPU utilization, or memory ceiling) and reproduce your baseline on a representative workload. You get a one-page scoping memo: what we're targeting, how we'll measure, and what we expect to find.
codeflash-agent runs autonomously in a sandbox against a snapshot of your codebase. It profiles, hypothesizes, rewrites, tests, and benchmarks, 24/7, in parallel, across hundreds of functions. Your engineers don't drive it. They review the output. Weekly sync to see what's surfacing.
Every candidate is read by a Codeflash performance engineer before it becomes a PR. We reject clever-but-fragile rewrites, changes that trade readability for minor gains, and anything that can't be cleanly reproduced. What you merge is the filtered shortlist, each PR with benchmarks, rationale, and reproduction instructions in the description.
"We've used Codeflash in the Pydantic codebase to optimize recursive algorithms and attribute access patterns. The thorough testing gives us confidence in merging the changes."
When the engagement ends, the agent stays. It watches every new PR in the repos we worked on, benchmarks the affected functions, and posts a suggested patch when it finds a regression or a win. New code starts optimal and the gains compound instead of decaying.
"The nice thing about it is it's not interfering with developers' existing workflows." — Crag Wolfe, Unstructured
The loop a senior performance engineer would run by hand, except the agent runs it 24/7 across hundreds of functions without tiring. See the full comparison →
Being upfront about the operational constraints before you sign anything.
The agent measures. It doesn't estimate. If your performance problem can't be expressed as a reproducible benchmark script, we'll help you write one before the engagement starts. We won't quote results from a workload we haven't measured ourselves.
If your workload depends on a specific GPU SKU, instance family, memory configuration, or CPU architecture, we mirror that in our sandbox before running. Optimization results on different hardware can diverge significantly. We say this clearly rather than hiding it in the small print.
The agent runs against a snapshot of your repo in our sandbox. We never connect to your production database, live traffic, or running services. If your bottleneck only manifests under production load patterns, we'll need a benchmark that reproduces that pattern synthetically.
When the agent surfaces something that looks like a constraint rather than waste (a defensive pattern, a hardcoded limit, a service boundary), we need someone on your side who can tell us within a day whether to explore it or skip it. Usually 30 minutes a week. If that person is in two weeks of feature freeze, we adjust the start date.
Some workloads are too variable to benchmark reliably at our end — highly stateful services, workloads that require authenticated live data, or pipelines with non-deterministic timing. We'll discover this in the first week. When we do, we'll scope around it or refund the diagnostic.
The engagement and Continuous Optimization are designed to run in sequence. One fixes the existing problem; the other makes sure new code doesn't create it again.
A scoped, time-bounded engagement. The agent finds the waste across your production system and our engineers review and ship every fix. ROI guaranteed before you commit.
See the Optimization Engagement →The same agent runs on every PR your team opens. Regressions are caught before they merge. Rewrites are suggested with the numbers to prove them.
See Continuous Optimization →We'll tell you where the waste is and what we'd target, before you commit to anything.
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